Think Different

  In machine learning, activation functions are crucial components of artificial neural networks. They introduce non-linearity into the network, enabling it to learn and represent complex patterns in data. Here's a breakdown of the concept and examples of common activation functions: 1. What is an Activation Function? Purpose: Introduces non-linearity into a neural network, allowing it to model complex relationships and make better predictions. Position: Located within each neuron of a neural network, applied to the weighted sum of inputs before passing the output to the next layer. 2. Common Activation Functions and Examples: a. Sigmoid : Output: S-shaped curve between 0 and 1. Use Cases: Binary classification, historical use in early neural networks. Example: Predicting if an image contains a cat (output close to 1) or not (output close to 0). b. Tanh (Hyperbolic Tangent): Output: S-shaped curve between -1 and 1. Use Cases: Similar to sigmoid, often ...